EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

2020 8th International Winter Conference on Brain-Computer Interface (BCI)

2020

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Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography signals are distorted by movement artifacts and electromyography signals in ambulatory condition, which make hard to recognize human intention. In addition, as hardware issues are also challenging, ear-EEG has been developed for practical brain-computer interface and is widely used. However, ear-EEG still contains contaminated signals. In this paper, we proposed robust two-stream deep neural networks in walking conditions and analyzed the visual response EEG signals in the scalp and ear in terms of statistical analysis and braincomputer interface performance. We validated the signals with the visual response paradigm, steady-state visual evoked potential. The brain-computer interface performance deteriorated as 3~14% when walking fast at 1.6 m/s. When applying the proposed method, the accuracies increase 15% in cap-EEG and 7% in ear-EEG. The proposed method shows robust to the ambulatory condition in session dependent and session-to-session experiments.

Section: B Experimental Paradigmmentioning

confidence: 99%

Decoding Visual Responses based on Deep Neural Networks with Ear-EEG Signals

Leel

2020 8th International Winter Conference on Brain-Computer Interface (BCI)

2020

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“…The presentation order was randomized, and non-target stimuli were flashed consecutively in order to avoid the repeated flashing of the same target stimulus between the sequences. The paradigm code was developed with the Psychophysics Toolbox and OpenBMI [30] in Matlab (MathWorks, MA, USA).…”

Section: B Experimental Proceduresmentioning

confidence: 99%

A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task

Kim

2020 8th International Winter Conference on Brain-Computer Interface (BCI)

2020

Self Cite

Recently, smart home systems based on braincomputer interface (BCI) has attracted a wide range of interests in both industry and academia. However, the current BCI system has several shortcomings as it produces a comparatively lower accuracy for real-time implementations as well as the intuitive paradigm for the users cannot be well established here. Therefore, in this study, we proposed a highly intuitive BCI paradigm that combines event-related potential (ERP) with the speech-imagery task for the individual target objects. The decoding accuracy of the proposed paradigm was 88.1% ( ± 5.90) which is a much significant higher performance than a conventional ERP system. Furthermore, the amplitude of N700 components was significantly enhanced over frontal regions which are priory evoked by the speech-imagery task. Our results could be utilized to develop a smart home system so that it could be more user-friendly and convenient by means of delivering user's intentions both, intuitively and accurately. Keywords-smart home control system; electroencephalography; event-related potential; imagined speechI.

“…EEG signal processing was conducted using a BBCI toolbox [47] and an OpenBMI toolbox [48] in a MATLAB 2019a environment. The EEG data were band-pass filtered between 1 to 50 Hz using a second-order zero-phase Butterworth filter and were down-sampled from 1000 Hz to 100 Hz.…”

Section: Eeg Pre-processingmentioning

confidence: 99%

Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

Jeong

et al. 2019

Brain Sciences

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Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.